Researching Multisystemic Resilience: A Sample Methodology
Bibliographic record
Abstract
In contexts of exposure to atypical stress or adversity, individual and collective resilience refers to the process of sustaining wellbeing by leveraging biological, psychological, social and environmental protective and promotive factors and processes (PPFPs). This multisystemic understanding of resilience is generating significant interest but has been difficult to operationalize in psychological research where studies tend to address only one or two systems at a time, often with a primary focus on individual coping strategies. We show how multiple systems implicated in human resilience can be researched in the same study using a longitudinal, six-phase transformative sequential mixed methods study of 14- to 24-year-olds and their elders in two communities dependent on oil and gas industries (Drayton Valley, Canada, and Secunda/eMbalenhle, South Africa). Data collection occurred over a 5-year period, and included: (1) community engagement and the identification of youth health and well-being priorities; (2) participatory youth-centric qualitative research using one-on-one semi-structured interviews and arts-based methods; (3) survey of 500 youth at three time points to assess psychosocial health indicators and outcomes; (4) collection of hair samples to assess stress biomarkers (cortisol and dehydroepiandrosterone-DHEA) over time; (5) youth-led ecological data collection and assessment of historical socio-economic development data; and (6) community resource mapping with community elders. Analyzing data from these multiple systems will allow us to understand the interrelationship and impact of PPFPs within and across systems. To date, we have undertaken thematic and narrative qualitative analyses, and descriptive analyses of the preliminary ecological and survey data. As we proceed, we will combine these and grounded theory approaches with innovative techniques such as latent transition analysis and network analysis, as well as modeling of economic conditions and spatial analysis of human geographies to understand patterns of PPFPs and their inter-relationships. By analyzing the complexity of data collected across systems (including cultural contexts) we are demonstrating the possibility of conducting multisystemic resilience research which expands the way psychological research accounts for positive development under stress in different contexts. This comprehensive examination of resilience may offer an example of how the study of resilience can inform socially and contextually relevant interventions and policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".